Tabular Data classification
Tabular data classification is a type of machine learning problem.
Each example consists of a list of features, each of which is either numeric or categorical:
- Numeric features are integer or floating-point numbers.
- Examples:
[1, 5, -2, 3,...][20.1, 12.5, -2.2, ...]
- Categorical features are values from a finite list of options.
- Examples:
['dog', 'cat', 'cat', 'dog', ...](options:{'dog', 'cat'})[0, 1, 2, 0, ..](options:{0, 1, 2})
- Note that categorical features can take integer values, as in the second example above. Unlike in the case of a number feature, however, the order of the numbers is not meaningful.
The task is to predict a class label from a finite list of possible classes, using the information in the features.